Some knowledge of econometrics, as well as basic familiarity with Stata is necessary, and experience with SQL and Spark would be a plus. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. We compare our system against strong baselines on publicly available datasets, and show that it produces summaries which are more informative, diverse and coherent. We develop a method for ranking these summaries according to desired criteria, coherence in our case, enabling our system to almost entirely avoid the problem of self-contradiction. A key component of the PASS system relies on applying systematic perturbations to the model’s input during inference, which allows it to generate multiple different summaries per product. We propose the PASS system (Perturb-and-Select Summarizer) that employs a large pre-trained Transformer-based model (T5 in our case), which follows a few-shot fine-tuning scheme. Second, as reviewers often disagree on the pros and cons of a given product, summarizers sometimes yield inconsistent, self-contradicting summaries. First, summarizers tend to favor generic content that appears in reviews for many different products, resulting in template-like, less informative summaries. This challenging task gives rise to two shortcomings in existing work. Such summaries are expected to aggregate a range of different opinions in a concise, coherent and informative manner. The product reviews summarization task aims to automatically produce a short summary for a set of reviews of a given product.

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